
import pandas as pd
import numpy as np
from collections import Counter
from wordcloud import WordCloud
import seaborn as sns
import plotly.express as px
import plotly.figure_factory as ff
import matplotlib.pyplot as plt
%matplotlib inline


#--------Movie Lens Data directories-----------
MLens = "../Data/ml-latest-small/movies.csv"
MLensLinks = "../Data/ml-latest-small/links.csv"
MLensRatings = "../Data/ml-latest-small/ratings.csv"
MLensTags = "../Data/ml-latest-small/tags.csv"
#--------imdb Data directories-----------------
IMDB = "../Data/iMDB/IMDb movies.csv"
IMDBName = "../Data/iMDB/IMDb names.csv"
IMDBRatings = "../Data/iMDB/IMDb ratings.csv"
IMDBPrincipals = "../Data/iMDB/IMDb title_principals.csv"


movieLensdf = pd.read_csv(MLens)
linksdf = pd.read_csv(MLensLinks)
mLensRatingsdf = pd.read_csv(MLensRatings)
mLensTagsdf = pd.read_csv(MLensTags)


movieLensdf.head()


movieLensdf.dtypes

movieId     int64
title      object
genres     object
dtype: object


linksdf.head()


linksdf = linksdf.drop("tmdbId",axis=1)


linksdf.dtypes

movieId    int64
imdbId     int64
dtype: object


linksdf.head()


mLensRatingsdf.head()


mLensRatingsdf.dtypes

userId         int64
movieId        int64
rating       float64
timestamp      int64
dtype: object


mLensTagsdf.head()


mLensTagsdf.dtypes

userId        int64
movieId       int64
tag          object
timestamp     int64
dtype: object


imdbdf = pd.read_csv(IMDB,low_memory = False)
imdbNamesdf = pd.read_csv(IMDBName,low_memory = False)
imdbRatingsdf = pd.read_csv(IMDBRatings,low_memory = False)
imdbPrincipalsdf = pd.read_csv(IMDBPrincipals,low_memory = False)


imdbdf.dtypes

imdb_title_id             object
title                     object
original_title            object
year                      object
date_published            object
genre                     object
duration                   int64
country                   object
language                  object
director                  object
writer                    object
production_company        object
actors                    object
description               object
avg_vote                 float64
votes                      int64
budget                    object
usa_gross_income          object
worlwide_gross_income     object
metascore                float64
reviews_from_users       float64
reviews_from_critics     float64
dtype: object


imdbdf.head()


imdbdf.count()

imdb_title_id            85855
title                    85855
original_title           85855
year                     85855
date_published           85855
genre                    85855
duration                 85855
country                  85791
language                 85022
director                 85768
writer                   84283
production_company       81400
actors                   85786
description              83740
avg_vote                 85855
votes                    85855
budget                   23710
usa_gross_income         15326
worlwide_gross_income    31016
metascore                13305
reviews_from_users       78258
reviews_from_critics     74058
dtype: int64


imdbdf.apply(lambda x: sum(x.isna()),axis=0)

imdb_title_id                0
title                        0
original_title               0
year                         0
date_published               0
genre                        0
duration                     0
country                     64
language                   833
director                    87
writer                    1572
production_company        4455
actors                      69
description               2115
avg_vote                     0
votes                        0
budget                   62145
usa_gross_income         70529
worlwide_gross_income    54839
metascore                72550
reviews_from_users        7597
reviews_from_critics     11797
dtype: int64


corr=imdbdf.corr()
fig, ax =plt.subplots(figsize=(12,8))
sns.heatmap(corr, cmap="Reds",
        xticklabels=corr.columns,
        yticklabels=corr.columns,
        square=False   )
plt.show()


imdbNamesdf.head()


imdbNamesdf.dtypes

imdb_name_id              object
name                      object
birth_name                object
height                   float64
bio                       object
birth_details             object
date_of_birth             object
place_of_birth            object
death_details             object
date_of_death             object
place_of_death            object
reason_of_death           object
spouses_string            object
spouses                    int64
divorces                   int64
spouses_with_children      int64
children                   int64
dtype: object


imdbNamesdf.apply(lambda x: sum(x.isna()),axis=0)

imdb_name_id                  0
name                          0
birth_name                    0
height                   253024
bio                       93007
birth_details            187093
date_of_birth            187093
place_of_birth           193713
death_details            257772
date_of_death            257772
place_of_death           260667
reason_of_death          275011
spouses_string           252353
spouses                       0
divorces                      0
spouses_with_children         0
children                      0
dtype: int64


corr=imdbNamesdf.corr()
fig, ax =plt.subplots(figsize=(12,8))
sns.heatmap(corr, cmap="Reds",
        xticklabels=corr.columns,
        yticklabels=corr.columns,
        square=False   )
plt.savefig("../Figures/imdbNames.png")
plt.show()


imdbRatingsdf.head()


imdbRatingsdf.dtypes

imdb_title_id                 object
weighted_average_vote        float64
total_votes                    int64
mean_vote                    float64
median_vote                  float64
votes_10                       int64
votes_9                        int64
votes_8                        int64
votes_7                        int64
votes_6                        int64
votes_5                        int64
votes_4                        int64
votes_3                        int64
votes_2                        int64
votes_1                        int64
allgenders_0age_avg_vote     float64
allgenders_0age_votes        float64
allgenders_18age_avg_vote    float64
allgenders_18age_votes       float64
allgenders_30age_avg_vote    float64
allgenders_30age_votes       float64
allgenders_45age_avg_vote    float64
allgenders_45age_votes       float64
males_allages_avg_vote       float64
males_allages_votes          float64
males_0age_avg_vote          float64
males_0age_votes             float64
males_18age_avg_vote         float64
males_18age_votes            float64
males_30age_avg_vote         float64
males_30age_votes            float64
males_45age_avg_vote         float64
males_45age_votes            float64
females_allages_avg_vote     float64
females_allages_votes        float64
females_0age_avg_vote        float64
females_0age_votes           float64
females_18age_avg_vote       float64
females_18age_votes          float64
females_30age_avg_vote       float64
females_30age_votes          float64
females_45age_avg_vote       float64
females_45age_votes          float64
top1000_voters_rating        float64
top1000_voters_votes         float64
us_voters_rating             float64
us_voters_votes              float64
non_us_voters_rating         float64
non_us_voters_votes          float64
dtype: object


imdbRatingsdf.apply(lambda x: sum(x.isna()),axis=0)

imdb_title_id                    0
weighted_average_vote            0
total_votes                      0
mean_vote                        0
median_vote                      0
votes_10                         0
votes_9                          0
votes_8                          0
votes_7                          0
votes_6                          0
votes_5                          0
votes_4                          0
votes_3                          0
votes_2                          0
votes_1                          0
allgenders_0age_avg_vote     52496
allgenders_0age_votes        52496
allgenders_18age_avg_vote      706
allgenders_18age_votes         706
allgenders_30age_avg_vote       10
allgenders_30age_votes          10
allgenders_45age_avg_vote       80
allgenders_45age_votes          80
males_allages_avg_vote           1
males_allages_votes              1
males_0age_avg_vote          58444
males_0age_votes             58444
males_18age_avg_vote          1465
males_18age_votes             1465
males_30age_avg_vote            12
males_30age_votes               12
males_45age_avg_vote           101
males_45age_votes              101
females_allages_avg_vote        81
females_allages_votes           81
females_0age_avg_vote        63738
females_0age_votes           63738
females_18age_avg_vote        6521
females_18age_votes           6521
females_30age_avg_vote         944
females_30age_votes            944
females_45age_avg_vote        2798
females_45age_votes           2798
top1000_voters_rating          679
top1000_voters_votes           679
us_voters_rating               209
us_voters_votes                209
non_us_voters_rating             1
non_us_voters_votes              1
dtype: int64


votes_columns = np.array([ "votes_{}".format(i) for i in (range(1,11))])
imdbRatingsdf[votes_columns].head()


fig, ax = plt.subplots(figsize=(12,8))
imdbRatingsdf[votes_columns].sum().plot.bar(ax)
ax.set_ylabel("Voters count")
ax.set_xlabel("score given")
ax.set_title("Number of voters for each score")
plt.savefig("../Figures/scoresDistribution.png")
plt.show()


corr=imdbRatingsdf.corr()
fig, ax =plt.subplots(figsize=(24,16))
sns.heatmap(corr, cmap="Reds",
        xticklabels=corr.columns,
        yticklabels=corr.columns,
        square=False   )
plt.show()


imdbPrincipalsdf.head(10)


imdbPrincipalsdf.dtypes

imdb_title_id    object
ordering          int64
imdb_name_id     object
category         object
job              object
characters       object
dtype: object


imdbPrincipalsdf.apply(lambda x: sum(x.isna()),axis=0)

imdb_title_id         0
ordering              0
imdb_name_id          0
category              0
job              622782
characters       494677
dtype: int64


mLensRatingsdf.head()


imdbRatingsdf.head()


linksdf.head()


linksdf['imdbId'] = linksdf["imdbId"].apply(lambda x: "tt{:07d}".format(x))
linksdf.head()


mergedmovies = linksdf.merge(imdbRatingsdf, left_on="imdbId", right_on="imdb_title_id")


mergedmovies


mergedDF = mLensRatingsdf.merge(mergedmovies, left_on="movieId", right_on="movieId")


mergedDF.head()


mergedDF.columns

Index(['userId', 'movieId', 'rating', 'timestamp', 'imdbId', 'imdb_title_id',
       'weighted_average_vote', 'total_votes', 'mean_vote', 'median_vote',
       'votes_10', 'votes_9', 'votes_8', 'votes_7', 'votes_6', 'votes_5',
       'votes_4', 'votes_3', 'votes_2', 'votes_1', 'allgenders_0age_avg_vote',
       'allgenders_0age_votes', 'allgenders_18age_avg_vote',
       'allgenders_18age_votes', 'allgenders_30age_avg_vote',
       'allgenders_30age_votes', 'allgenders_45age_avg_vote',
       'allgenders_45age_votes', 'males_allages_avg_vote',
       'males_allages_votes', 'males_0age_avg_vote', 'males_0age_votes',
       'males_18age_avg_vote', 'males_18age_votes', 'males_30age_avg_vote',
       'males_30age_votes', 'males_45age_avg_vote', 'males_45age_votes',
       'females_allages_avg_vote', 'females_allages_votes',
       'females_0age_avg_vote', 'females_0age_votes', 'females_18age_avg_vote',
       'females_18age_votes', 'females_30age_avg_vote', 'females_30age_votes',
       'females_45age_avg_vote', 'females_45age_votes',
       'top1000_voters_rating', 'top1000_voters_votes', 'us_voters_rating',
       'us_voters_votes', 'non_us_voters_rating', 'non_us_voters_votes'],
      dtype='object')


imdbdf_clean = imdbdf[['title','year', 'genre', 'duration', 'country', 'director', 'writer', 'production_company', 'actors', 'avg_vote']] #keep the wanted column


imdbdf_clean


#imdbdf_clean[imdbdf_clean["year"] == 'TV Movie 2019']["year"].apply(lambda x: x[-4:])
imdbdf_clean = imdbdf_clean[imdbdf_clean.year != 'TV Movie 2019']
imdbdf_clean['year'] = imdbdf_clean['year'].astype('int')


imdbdf_clean.isna().sum() #find all the NaN values

title                    0
year                     0
genre                    0
duration                 0
country                 64
director                87
writer                1572
production_company    4455
actors                  69
avg_vote                 0
dtype: int64


imdbdf_clean = imdbdf_clean.dropna() #drop all the rows with NaN values
imdbdf_clean.isna().sum()

title                 0
year                  0
genre                 0
duration              0
country               0
director              0
writer                0
production_company    0
actors                0
avg_vote              0
dtype: int64


countries = {} #create empty dictionary
list_countries = list(imdbdf_clean['country']) #set up a list of countries
for i in list_countries:
    i = list(i.split(',')) #split countries separated by commas
    if len(i) == 1: #if 1 country in countries.keys()
        if i in list(countries.keys()): #countries.keys() is the country name, countries.values() is the count of country name
            countries[i] +=1 #count
        else:
            countries[i[0]] = 0
    else:
        for j in i: #does the same but for len(i) != 1
            if j in list(countries.keys()):
                countries[j] += 1 #count
            else:
                countries[j] =1


genre = list(imdbdf_clean['genre'])
genre_list = [] #create an empty list

for i in genre:
    i = list(i.split(',')) #split words when comma
    for j in i:
        genre_list.append(j.replace(' ', '')) #replace extra space
        
genre_counter = Counter(genre_list) #We'll need it after


imdbdf_clean.describe()


#groupby year and count how many title we have each year
title = imdbdf_clean.groupby('year').agg({'title': ['count']})
title.columns = ['Title Count']
title = title.sort_values('Title Count', ascending = False)
title.head(5)


#visualize with a bar graph in descending title count order 
fig_dim = (25,12)
fig, ax = plt.subplots(figsize = fig_dim)
sns.countplot(x = 'year', data = imdbdf_clean, order = imdbdf_clean['year'].value_counts().index)
plt.title('Title count per year, descending sorting per number of title released')
plt.xticks(rotation=90)
plt.show()


#visualize with a plot graph in ascending year order
fig_dim = (25,12)
fig, ax = plt.subplots(figsize = fig_dim)
ax = sns.countplot(x = 'year', data = imdbdf_clean)
plt.title('Title count per year')

#display the count on the bar graph
for p in ax.patches:
    ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x(), p.get_height() + 20), rotation=90)
    
plt.xticks(rotation=90)
plt.show()


title


title = title.sort_index() #sort by year. The year is the index
growth = title.pct_change() #calculate the %growth year by year
growth.columns = ['% growth']
plt.figure(figsize = (15, 8))
x = growth['% growth']
y = growth.index
plt.bar(y, x)
plt.plot(y, x, color = 'red', linewidth = 2.0)
plt.title('Movie growth per year')
plt.xlabel('Year')
plt.ylabel('% growth')
plt.show()


imdbdf_clean = imdbdf_clean.sort_values(by = 'avg_vote', ascending = False)
rating = imdbdf_clean[0:10] #get the 1st 10 values
fig = px.sunburst(rating, path = ['year', 'country', 'title'], values = 'avg_vote', color = 'avg_vote')
fig.show()


rating = imdbdf_clean[imdbdf_clean.year > 1999][0:10] #get the 1st 10 values
fig = px.sunburst(rating, path = ['year', 'country', 'title'], values = 'avg_vote', color = 'avg_vote')
fig.show()


best = imdbdf_clean.groupby('year').agg({'avg_vote': ['mean']})
best.columns = ['Average rating']
best = best.sort_values('Average rating', ascending = False)
best.head()


plt.figure(figsize = (15, 8))
x = best['Average rating'].round(decimals = 3)
y = best.index
plt.bar(y, x)
plt.title('Average rating per year')
plt.xlabel('Year')
plt.ylabel('Average rating (on a scale of 10)')
plt.show()


best = best[best.index >=2000]


plt.figure(figsize = (15, 8))
x = best['Average rating'].round(decimals = 3)
y = best.index
plt.bar(y, x)
plt.title('Average rating per year')
plt.xlabel('Year')
plt.ylabel('Average rating (on a scale of 10)')
plt.show()


countries_fin = {} #create an empty dictionary
for country, no in countries.items(): 
    country = country.replace(' ', '') #remove the extra space create by the split(',')
    if country in list(countries_fin.keys()):
        countries_fin[country] += no
    else:
        countries_fin[country] = no

#sort the country according to their count
countries_fin = {k: v for k, v in sorted(countries_fin.items(), key = lambda item: item[1], reverse= True)}


plt.figure(figsize = (8,8))
ax = sns.barplot(x = list(countries_fin.keys())[0:10], y = list(countries_fin.values())[0:10])
plt.title('Top 10 countries creating movies')
plt.xticks(rotation=45)
#display the count on the bar graph
for p in ax.patches:
    ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x() + 0.1, p.get_height() + 20))
    
plt.show()


#create the dataframe
df_countries = pd.DataFrame(list(countries_fin.items()), columns = ['Country', 'Country count'])

#plot a pie chart of the distribution of each country per region
fig,ax = plt.subplots()
x = df_countries['Country count'][:10] #get the top 10 countries
labels = df_countries['Country'][:10] #get the top 10 countries
ax.pie(x, labels = labels, radius = 2)

#create a white circle at the center of the pie to create a donut chart
my_circle = plt.Circle( (0,0), 0.3, color = 'white')
p = plt.gcf()
p.gca().add_artist(my_circle)

plt.show()


genres = {k: v for k, v in sorted(genre_counter.items(), key=lambda item: item[1], reverse= True)}
genres

{'Drama': 44166,
 'Comedy': 27475,
 'Romance': 13233,
 'Action': 12084,
 'Thriller': 10706,
 'Crime': 10541,
 'Horror': 8896,
 'Adventure': 7302,
 'Mystery': 4933,
 'Family': 3723,
 'Fantasy': 3585,
 'Sci-Fi': 3411,
 'Biography': 2314,
 'History': 2190,
 'War': 2132,
 'Animation': 1994,
 'Musical': 1907,
 'Music': 1620,
 'Western': 1556,
 'Sport': 1025,
 'Film-Noir': 661,
 'Reality-TV': 2,
 'Documentary': 1,
 'News': 1}


fig_dim = (15,10)
fig, ax = plt.subplots(figsize = fig_dim)
x = list(genres.keys())
y = list(genres.values())
ax.vlines(x, ymin = 0, ymax = y, linewidth = 8)
plt.xticks(rotation = 90)
plt.show()


#create list of genre (unique genre)
text = list(genres.keys())

#create the wordcloud object
wordcloud = WordCloud(width = 500, height = 500, max_words = 100000, background_color = 'white').generate(str(text))

#Display the generated image
plt.imshow(wordcloud, interpolation = 'bilinear')
plt.axis('off')
plt.margins(x = 0, y = 0)
plt.show()


config = {'responsive': False}
genres_df = pd.DataFrame({"Genre":genres.keys(),"number_of_movies":genres.values()})

genres_bar = px.bar(genres_df[:10], 
                    x = 'Genre', 
                    y = 'number_of_movies', 
                    title = 'Top 10 Genres in terms of number of movies',
                    text = 'number_of_movies', 
                    labels = dict(genre = 'Genre', number_of_movies = 'Number of movies'),
                    color = 'Genre')
                    
genres_bar.show()


years_df = imdbdf_clean[['year','title', 'genre']].groupby(['year','genre']).count().reset_index().rename(columns={'title':'number_of_movies'})
years_df = years_df.sort_values(by=['number_of_movies'], ascending=False)
years_df = years_df[years_df['year'] >= 2000]#Movies of this century 
years_df = years_df[years_df['number_of_movies'] >= 100]#years where the number of movies is over 100


detailed_genres_bar = px.bar(years_df, 
                             x ='year', 
                             y = 'number_of_movies', 
                             color = 'genre', 
                             title='Movies produced in the 21st century classified by genres', 
                             text = 'number_of_movies',
                             labels = dict(year = 'Year', number_of_movies = 'Number of movies')
                             )
detailed_genres_bar.show()


production_companies_df = imdbdf_clean[['production_company','title']].groupby(['production_company']).count().reset_index().rename(columns={'title':'number_of_movies'})
production_companies_df = production_companies_df.sort_values(by='number_of_movies', ascending=False)
production_companies_df = production_companies_df.iloc[:10]
production_companies_df


detailed_genres_bar = px.bar(production_companies_df, 
                             x ='production_company', 
                             y = 'number_of_movies', 
                             color = 'production_company', 
                             title='Top 10 production companies', 
                             text = 'number_of_movies',
                             labels = dict(production_company = 'Production Company', number_of_movies = 'Number of movies')
                             )
detailed_genres_bar.show()


production_companies_df = imdbdf_clean[imdbdf_clean.year > 1999][['production_company','title']].groupby(['production_company']).count().reset_index().rename(columns={'title':'number_of_movies'})
production_companies_df = production_companies_df.sort_values(by='number_of_movies', ascending=False)
production_companies_df = production_companies_df.iloc[:10]
detailed_genres_bar = px.bar(production_companies_df, 
                             x ='production_company', 
                             y = 'number_of_movies', 
                             color = 'production_company', 
                             title='Top 10 production companies', 
                             text = 'number_of_movies',
                             labels = dict(production_company = 'Production Company', number_of_movies = 'Number of movies')
                             )
detailed_genres_bar.show()

	movieId	title	genres
0	1	Toy Story (1995)	Adventure\|Animation\|Children\|Comedy\|Fantasy
1	2	Jumanji (1995)	Adventure\|Children\|Fantasy
2	3	Grumpier Old Men (1995)	Comedy\|Romance
3	4	Waiting to Exhale (1995)	Comedy\|Drama\|Romance
4	5	Father of the Bride Part II (1995)	Comedy

	movieId	imdbId	tmdbId
0	1	114709	862.0
1	2	113497	8844.0
2	3	113228	15602.0
3	4	114885	31357.0
4	5	113041	11862.0

	movieId	imdbId
0	1	114709
1	2	113497
2	3	113228
3	4	114885
4	5	113041

	userId	movieId	rating	timestamp
0	1	1	4.0	964982703
1	1	3	4.0	964981247
2	1	6	4.0	964982224
3	1	47	5.0	964983815
4	1	50	5.0	964982931

	userId	movieId	tag	timestamp
0	2	60756	funny	1445714994
1	2	60756	Highly quotable	1445714996
2	2	60756	will ferrell	1445714992
3	2	89774	Boxing story	1445715207
4	2	89774	MMA	1445715200

Data Directories:¶

EDA:¶

MovieLens Data:¶

IMDb Data:¶

IMDb movies:¶

IMDb names:¶

IMDb Ratings:¶

IMDb Principals:¶

Merging it together:¶

Filtering the data needed:¶

Working on the IMDb data only:¶

	imdb_title_id	title	original_title	year	date_published	genre	duration	country	language	director	...	actors	description	avg_vote	votes	budget	usa_gross_income	worlwide_gross_income	metascore	reviews_from_users	reviews_from_critics
0	tt0000009	Miss Jerry	Miss Jerry	1894	1894-10-09	Romance	45	USA	None	Alexander Black	...	Blanche Bayliss, William Courtenay, Chauncey D...	The adventures of a female reporter in the 1890s.	5.9	154	NaN	NaN	NaN	NaN	1.0	2.0
1	tt0000574	The Story of the Kelly Gang	The Story of the Kelly Gang	1906	1906-12-26	Biography, Crime, Drama	70	Australia	None	Charles Tait	...	Elizabeth Tait, John Tait, Norman Campbell, Be...	True story of notorious Australian outlaw Ned ...	6.1	589	$ 2250	NaN	NaN	NaN	7.0	7.0
2	tt0001892	Den sorte drøm	Den sorte drøm	1911	1911-08-19	Drama	53	Germany, Denmark	NaN	Urban Gad	...	Asta Nielsen, Valdemar Psilander, Gunnar Helse...	Two men of high rank are both wooing the beaut...	5.8	188	NaN	NaN	NaN	NaN	5.0	2.0
3	tt0002101	Cleopatra	Cleopatra	1912	1912-11-13	Drama, History	100	USA	English	Charles L. Gaskill	...	Helen Gardner, Pearl Sindelar, Miss Fielding, ...	The fabled queen of Egypt's affair with Roman ...	5.2	446	$ 45000	NaN	NaN	NaN	25.0	3.0
4	tt0002130	L'Inferno	L'Inferno	1911	1911-03-06	Adventure, Drama, Fantasy	68	Italy	Italian	Francesco Bertolini, Adolfo Padovan	...	Salvatore Papa, Arturo Pirovano, Giuseppe de L...	Loosely adapted from Dante's Divine Comedy and...	7.0	2237	NaN	NaN	NaN	NaN	31.0	14.0

	imdb_name_id	name	birth_name	height	bio	birth_details	date_of_birth	place_of_birth	death_details	date_of_death	place_of_death	reason_of_death	spouses_string	spouses	divorces	spouses_with_children	children
0	nm0000001	Fred Astaire	Frederic Austerlitz Jr.	177.0	Fred Astaire was born in Omaha, Nebraska, to J...	May 10, 1899 in Omaha, Nebraska, USA	1899-05-10	Omaha, Nebraska, USA	June 22, 1987 in Los Angeles, California, USA ...	1987-06-22	Los Angeles, California, USA	pneumonia	Robyn Smith (27 June 1980 - 22 June 1987) (hi...	2	0	1	2
1	nm0000002	Lauren Bacall	Betty Joan Perske	174.0	Lauren Bacall was born Betty Joan Perske on Se...	September 16, 1924 in The Bronx, New York City...	1924-09-16	The Bronx, New York City, New York, USA	August 12, 2014 in New York City, New York, US...	2014-08-12	New York City, New York, USA	stroke	Jason Robards (4 July 1961 - 10 September 196...	2	1	2	3
2	nm0000003	Brigitte Bardot	Brigitte Bardot	166.0	Brigitte Bardot was born on September 28, 1934...	September 28, 1934 in Paris, France	1934-09-28	Paris, France	NaN	NaN	NaN	NaN	Bernard d'Ormale (16 August 1992 - present)\n...	4	3	1	1
3	nm0000004	John Belushi	John Adam Belushi	170.0	John Belushi was born in Chicago, Illinois, US...	January 24, 1949 in Chicago, Illinois, USA	1949-01-24	Chicago, Illinois, USA	March 5, 1982 in Hollywood, Los Angeles, Calif...	1982-03-05	Hollywood, Los Angeles, California, USA	acute cocaine and heroin intoxication	Judith Belushi-Pisano (31 December 1976 - 5 M...	1	0	0	0
4	nm0000005	Ingmar Bergman	Ernst Ingmar Bergman	179.0	Ernst Ingmar Bergman was born July 14, 1918, t...	July 14, 1918 in Uppsala, Uppsala län, Sweden	1918-07-14	Uppsala, Uppsala län, Sweden	July 30, 2007 in Fårö, Gotlands län, Sweden (...	2007-07-30	Fårö, Gotlands län, Sweden	natural causes	Ingrid Bergman (11 November 1971 - 20 May 199...	5	4	5	8

	imdb_title_id	weighted_average_vote	total_votes	mean_vote	median_vote	votes_10	votes_9	votes_8	votes_7	votes_6	...	females_30age_avg_vote	females_30age_votes	females_45age_avg_vote	females_45age_votes	top1000_voters_rating	top1000_voters_votes	us_voters_rating	us_voters_votes	non_us_voters_rating	non_us_voters_votes
0	tt0000009	5.9	154	5.9	6.0	12	4	10	43	28	...	5.7	13.0	4.5	4.0	5.7	34.0	6.4	51.0	6.0	70.0
1	tt0000574	6.1	589	6.3	6.0	57	18	58	137	139	...	6.2	23.0	6.6	14.0	6.4	66.0	6.0	96.0	6.2	331.0
2	tt0001892	5.8	188	6.0	6.0	6	6	17	44	52	...	5.8	4.0	6.8	7.0	5.4	32.0	6.2	31.0	5.9	123.0
3	tt0002101	5.2	446	5.3	5.0	15	8	16	62	98	...	5.5	14.0	6.1	21.0	4.9	57.0	5.5	207.0	4.7	105.0
4	tt0002130	7.0	2237	6.9	7.0	210	225	436	641	344	...	7.3	82.0	7.4	77.0	6.9	139.0	7.0	488.0	7.0	1166.0

	votes_1	votes_2	votes_3	votes_4	votes_5	votes_6	votes_7	votes_8	votes_9	votes_10
0	14	5	1	9	28	28	43	10	4	12
1	16	13	20	28	103	139	137	58	18	57
2	4	6	5	16	32	52	44	17	6	6
3	16	25	26	63	117	98	62	16	8	15
4	87	20	39	66	169	344	641	436	225	210

	imdb_title_id	ordering	imdb_name_id	category	job	characters
0	tt0000009	1	nm0063086	actress	NaN	["Miss Geraldine Holbrook (Miss Jerry)"]
1	tt0000009	2	nm0183823	actor	NaN	["Mr. Hamilton"]
2	tt0000009	3	nm1309758	actor	NaN	["Chauncey Depew - the Director of the New Yor...
3	tt0000009	4	nm0085156	director	NaN	NaN
4	tt0000574	1	nm0846887	actress	NaN	["Kate Kelly"]
5	tt0000574	2	nm0846894	actor	NaN	["School Master"]
6	tt0000574	3	nm3002376	actor	NaN	["Steve Hart"]
7	tt0000574	4	nm0170118	actress	NaN	NaN
8	tt0000574	5	nm0846879	director	NaN	NaN
9	tt0000574	6	nm0317210	producer	producer	NaN

	movieId	imdbId	imdb_title_id	weighted_average_vote	total_votes	mean_vote	median_vote	votes_10	votes_9	votes_8	...	females_30age_avg_vote	females_30age_votes	females_45age_avg_vote	females_45age_votes	top1000_voters_rating	top1000_voters_votes	us_voters_rating	us_voters_votes	non_us_voters_rating	non_us_voters_votes
0	1	tt0114709	tt0114709	8.3	864461	8.3	8.0	177024	214560	273073	...	8.2	59102.0	8.3	12842.0	8.1	839.0	8.6	147914.0	8.2	338585.0
1	2	tt0113497	tt0113497	7.0	299083	7.1	7.0	24313	22327	63008	...	7.2	25960.0	7.2	4219.0	6.8	792.0	7.0	49740.0	6.9	134663.0
2	3	tt0113228	tt0113228	6.7	23742	6.8	7.0	2270	1389	3441	...	6.9	1778.0	6.9	880.0	6.2	373.0	6.7	8332.0	6.6	8209.0
3	4	tt0114885	tt0114885	5.9	9353	6.2	6.0	1205	442	989	...	6.5	1784.0	6.6	970.0	5.2	214.0	6.2	3824.0	5.3	2301.0
4	5	tt0113041	tt0113041	6.1	33587	6.2	6.0	2371	1370	3186	...	6.4	5191.0	6.3	1188.0	5.5	382.0	6.1	9446.0	5.9	13165.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8557	193571	tt3110014	tt3110014	6.7	286	6.8	7.0	28	19	50	...	6.8	33.0	8.3	3.0	8.0	6.0	7.4	29.0	6.6	154.0
8558	193573	tt3837248	tt3837248	7.2	455	7.7	8.0	172	31	72	...	7.7	10.0	4.0	4.0	6.4	18.0	7.5	55.0	7.0	104.0
8559	193581	tt5476944	tt5476944	7.7	847	8.1	8.0	291	110	179	...	8.0	50.0	8.1	13.0	6.2	16.0	7.8	99.0	7.4	259.0
8560	193583	tt5914996	tt5914996	7.5	2504	7.9	8.0	699	375	553	...	7.1	33.0	5.8	13.0	5.5	28.0	7.1	232.0	7.4	757.0
8561	193587	tt8391976	tt8391976	7.2	521	7.5	8.0	109	45	113	...	7.8	33.0	7.3	3.0	6.4	10.0	7.1	46.0	6.8	183.0

	year	duration	avg_vote
count	80161.000000	80161.000000	80161.000000
mean	1992.965644	100.016342	5.913369
std	24.551436	22.023683	1.216666
min	1894.000000	41.000000	1.000000
25%	1977.000000	88.000000	5.300000
50%	2002.000000	96.000000	6.100000
75%	2013.000000	108.000000	6.800000
max	2020.000000	808.000000	9.800000

	Title Count
year
2017	3010
2018	2957
2016	2895
2015	2742
2014	2675
...	...
1913	11
1912	5
1911	4
1906	1
1894	1

	Average rating
year
1923	6.812121
1924	6.790196
1928	6.778313
1926	6.698592
1921	6.697872

	production_company	number_of_movies
20624	Metro-Goldwyn-Mayer (MGM)	1283
30637	Warner Bros.	1152
8179	Columbia Pictures	913
23166	Paramount Pictures	903
29674	Twentieth Century Fox	864
29942	Universal Pictures	732
24630	RKO Radio Pictures	535
21216	Mosfilm	278
29938	Universal International Pictures (UI)	272
6402	Canal+	231

	votes_1	votes_2	votes_3	votes_4	votes_5	votes_6	votes_7	votes_8	votes_9	votes_10
0	14	5	1	9	28	28	43	10	4	12
1	16	13	20	28	103	139	137	58	18	57
2	4	6	5	16	32	52	44	17	6	6
3	16	25	26	63	117	98	62	16	8	15
4	87	20	39	66	169	344	641	436	225	210

	votes_1	votes_2	votes_3	votes_4	votes_5	votes_6	votes_7	votes_8	votes_9	votes_10
0	14	5	1	9	28	28	43	10	4	12
1	16	13	20	28	103	139	137	58	18	57
2	4	6	5	16	32	52	44	17	6	6
3	16	25	26	63	117	98	62	16	8	15
4	87	20	39	66	169	344	641	436	225	210